{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "217daec3",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 2)\n",
      "[[1 2]\n",
      " [3 4]\n",
      " [5 6]]\n",
      "[1 2]\n",
      "[1 3 5]\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "tf.compat.v1.disable_eager_execution()\n",
    "data1 = tf.constant([[6,6]])\n",
    "data4= tf.constant([[2],[2]])\n",
    "data2 = tf.constant([[3,3]])\n",
    "data3 = tf.constant([[1,2],[3,4],[5,6]])\n",
    "print(data3.shape)\n",
    "\n",
    "with tf.compat.v1.Session() as sess:\n",
    "    print(sess.run(data3))\n",
    "    print(sess.run(data3[0]))\n",
    "    print(sess.run(data3[:,0]))\n",
    "    print(sess.run(data3[0,1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b76eb572",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[24]]\n",
      "[[5 5]\n",
      " [5 5]]\n",
      "[[12 12]\n",
      " [12 12]]\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "data1 = tf.constant([[6,6]])\n",
    "data2= tf.constant([[2],[2]])\n",
    "data3 = tf.constant([[3,3]])\n",
    "data4 = tf.constant([[1,2],[3,4],[5,6]])\n",
    "matMul = tf.matmul(data1,data2)\n",
    "matMul2 = tf.multiply(data1,data2)\n",
    "matAdd = tf.add(data2,data3)\n",
    "with tf.compat.v1.Session() as sess:\n",
    "    print(sess.run(matMul))\n",
    "    print(sess.run(matAdd))\n",
    "    print(sess.run(matMul2))\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "91af23ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([[0, 0, 0],\n",
      "       [0, 0, 0]]), array([[0., 0., 0.],\n",
      "       [0., 0., 0.]], dtype=float32), array([[1., 1., 1.],\n",
      "       [1., 1., 1.]], dtype=float32), array([[15, 15, 15],\n",
      "       [15, 15, 15]])]\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "mat0 = tf.constant([[0,0,0],[0,0,0]])\n",
    "mat1 = tf.zeros([2,3])\n",
    "mat2 = tf.ones([2,3])\n",
    "mat3 = tf.fill([2,3],15)\n",
    "with tf.compat.v1.Session() as sess:\n",
    "#     print(sess.run(mat0))\n",
    "    print(sess.run([mat0,mat1,mat2,mat3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e94598aa",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([[2],\n",
      "       [3],\n",
      "       [4]]), array([[0],\n",
      "       [0],\n",
      "       [0]]), array([0.       , 0.2      , 0.4      , 0.6      , 0.8      , 1.       ,\n",
      "       1.2      , 1.4      , 1.6      , 1.8000001, 2.       ],\n",
      "      dtype=float32)]\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "mat1 = tf.constant([[2],[3],[4]])\n",
    "mat2 = tf.zeros_like(mat1)\n",
    "mat3 = tf.linspace(0.0,2.0,11)\n",
    "with tf.compat.v1.Session() as sess:\n",
    "    print(sess.run([mat1,mat2,mat3]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51448692",
   "metadata": {},
   "source": [
    "numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "deac8323",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2]\n",
      " [3 4]]\n",
      "(5,) (2, 2)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "data = np.array([1,2,3,4,5])\n",
    "data2 = np.array([[1,2],[3,4]])\n",
    "print(data2)\n",
    "print(data.shape,data2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2207870c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from numpy import array\n",
    "x = np.array([1,2,3,4,5,6,7,8])\n",
    "y = np.array([3,5,7,6,2,6,10,15])\n",
    "plt.plot(x,y,'r')\n",
    "# plt.plot(x,y,'g',lw=10)\n",
    "# 折线 饼状 柱状\n",
    "x = np.array([1,2,3,4,5,6,7,8])\n",
    "y = np.array([13,25,17,36,21,16,10,15])\n",
    "plt.bar(x,y,0.2,alpha=1,color='b')# 5 color 4 透明度 3 0.9\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
